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The last decades have not only been characterized by an explosive growth ofdata, but also an increasing appreciation of data as a valuable resource. Theirvalue comes with the ability to extract meaningful patterns that are ofeconomic, societal or scientific relevance. A particular challenge is theidentification of patterns across time, including those that might only becomeapparent when the temporal dimension is taken into account. Here, we present anovel method that aims to achieve this by detecting dynamic clusters, i.e.structural elements that can be present over prolonged durations. It is basedon an adaptive identification of majority overlaps between groups at differenttime points and accommodates the transient decompositions in otherwisepersistent dynamic clusters. Our method enables the detection of persistentstructural elements with internal dynamics and can be applied to anyclassifiable data, ranging from social contact networks to arbitrary sets oftime stamped feature vectors. It represents a unique tool to study systems withnon-trivial temporal dynamics and has a broad applicability to scientific,societal and economic data.

A time resolved clustering method revealing longterm structures and their short-term internal dynamics